Performing Reproducible Translational Research By Integrating Immunomes And Exposomes

The immune system defends hosts from external and internal pathogenic agents and responds with inflammatory processes throughout the healing process. The environment plays a much greater role than genes in the development, adaptation and modulation of the immune system factors1–3. These combinatorial interactions between the immune system, the environment and the genome lead to variations in disease phenotypes and responses to therapeutic interventions. Therefore, understanding disease mechanisms and the development of interventions would need to consider the complex interplay of the endogenous immunological process along with the effects of the modern environment, thus requiring generation of a complete picture of environmental exposures, and immune responses along with genetic factors. The immunome is defined as the complete set of all genes and their translating immunological proteins, - many of them resulting from various environmental stimuli4–6. The exposome encompasses the life-course of environmental exposures (including lifestyle factors) from prenatal periods and complements the genome by providing a comprehensive description of lifelong exposure history 7 . Immunome research requires the integration of diverse data types for supporting different research use-cases. While there exist gaps and sparseness in data points needed to generate sufficiently complete immunomes and exposomes, using available data with an understanding of their limitations could enable reproducibility research. In order to systematically support these needs, we are developing a scalable computation infrastructure, the Utah PRISMS Informatics Ecosystem (UPIE)8, that enables generation, integration and utilization of immunomes and exposomes for translational research. UPIE is a semantically consistent, metadata-driven, event-based big data infrastructure. It includes methods and processes for collection of sensor and person-generated data; selection and invocation of computational models for filling spatio-temporal gaps in exposomic data; data integration; and participant and research facing tools. In this presentation, we discuss how our metadata driven and uncertainty-characterizing approaches within UPIE enable the generation of immunomes and exposomes, and how their integration is essential for reproducibility of research. We explain the generalizability of this multi-scale and multi-omics platform for providing robust pipelines for reproducible research in conditions such as asthma and type 1 diabetes mellitus using publicly available immunome data from sources such Immport9.
This research is partially supported by the Utah PRISMS Informatics Center through NIH/NIBIB U54EB021973, the Utah Center for Clinical and Translational Science funded by NCATS award UL1TR001067, the NIH Ruth L. Kirschstein NRS Award 5T32DK091317 from NIDDK, and the NLM Training grant T15LM007124.
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